A hybrid differential evolution algorithm for parameter tuning of evolving spiking neural network
In this paper, differential evolution (DE) has been utilised to solve the problem of tuning the parameters of evolving spiking neural network (ESNN) manually. As ESNN is sensitive to its parameters as other models, optimal integration of parameters leads to better classification accuracy. A hybrid d...
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| Main Authors: | , , |
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| Format: | Article |
| Published: |
Inderscience Publishers
2017
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/97057/ http://dx.doi.org/10.1504/IJCVR.2017.081231 |
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| Institution: | Universiti Teknologi Malaysia |